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Improved Condition Assessment through Statistical Analyses: Case Study of Railway Track
Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.ORCID iD: 0000-0003-3911-8009
Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
2017 (English)Report (Other academic)
Abstract [en]

Traditional practice within railway maintenance is based on engineering knowledge and practical experience, which are documented in regulations. This practice is often time-based, but can also be condition-based by combining time-based inspections with condition-based actions depending on the inspection results. However, the logic behind the resulting regulation is seldom well documented, which makes it challenging to optimise maintenance based on factors such as operational conditions or new technologies, methodologies and best practices. One way to deal with this challenge is to use statistical analysis and build models that support fault diagnostics and failure prognostics. This analysis approach will increase in importance as automated inspections replace manual inspections. Specific measurement equipment and trains are not the only ones producing automated measurements; regular traffic is increasingly often producing measurements. Hence, there will not be any lack of condition data, but the challenge will be to use this data in a correct way and to extract reliable information as decision support. In this context, it is crucial to balance the risks of false alarms and unrecognised faults, but also to estimate the quality of both data and information. The purpose of this work is to use statistics in order to support improved asset management, by building statistical models as a complement to physical models and engineering knowledge. The resulting models combine theories from the field of time-series analysis, statistical process control (SPC) and measurement system analysis. Charts and plots present results and have prognostic capabilities that allow necessary track possession times to be included in the timetable. 

Place, publisher, year, edition, pages
Luleå: Luleå University of Technology, 2017. , 80 p.
Series
Research report / Luleå University of Technology, ISSN 1402-1528
Keyword [en]
Fault Diagnostics, Failure Prognostics, Measurement System Analysis, Statastical Analysis, Statistical Modelling, Time Series Analysis, Statistical Process Control (SPC), Railway Track, Sweden
National Category
Reliability and Maintenance
Research subject
Quality Technology and Management
Identifiers
URN: urn:nbn:se:ltu:diva-64094ISBN: 978-91-7583-937-0 (print)ISBN: 978-91-7583-938-7 (electronic)OAI: oai:DiVA.org:ltu-64094DiVA: diva2:1110609
Projects
Fortsättningsprojekt: Förbättrad tillståndsbedömning genom statistisk analys
Funder
Swedish Transport Administration
Available from: 2017-06-16 Created: 2017-06-16 Last updated: 2017-11-24

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Bergquist, BjarneSöderholm, Peter

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